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Open AccessArticle

Water End Use Disaggregation Based on Soft Computing Techniques

ITA-Grupo de Ingeniería y Tecnología del Agua, Dpto. de Ingeniería del Agua y Medio Ambiente, Universitat Politècnica de València, Camino de Vera s/n, 46022 València, Spain
Author to whom correspondence should be addressed.
Water 2018, 10(1), 46;
Received: 22 November 2017 / Revised: 3 January 2018 / Accepted: 5 January 2018 / Published: 9 January 2018
Disaggregating residential water end use events through the available commercial tools needs a great investment in time to manually process smart metering data. Therefore, it is extremely difficult to achieve a homogenous and sufficiently large corpus of classified single-use events capable of accurately describe residential water consumption. The main goal of the present paper is to develop an automatic tool that facilitates the disaggregation of the individual water consumptions events from the raw flow trace. The proposed disaggregation methodology is conducted through two actions that are iteratively performed: first, the use of an advanced two-step filter, whose calibration is automatically conducted by the Elitist Non-Dominated Sorting Genetic Algorithm NSGA-II; and second, a cropping algorithm based on the filtered water consumption flow traces. As a secondary goal, yet complementary to the main one, a semiautomatic massive classification process has been developed, so that the resulting single-use events can be easily categorized in the different water end uses in a household. This methodology was tested using water consumption data from two different case studies. The characteristics of the households taken as reference and their occupants were unequivocally dissimilar from each other. In addition, the monitoring equipment used to obtain the consumption flow traces had completely different technical specifications. The results obtained from the processing of the two studies show that the automatic disaggregation is both robust and accurate, and produces significant time saving compared to the standard manual analysis. View Full-Text
Keywords: water end uses; water microcomponents; high frequency smart metering data; residential water flow trace disaggregation; water flow trace filtering water end uses; water microcomponents; high frequency smart metering data; residential water flow trace disaggregation; water flow trace filtering
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Pastor-Jabaloyes, L.; Arregui, F.J.; Cobacho, R. Water End Use Disaggregation Based on Soft Computing Techniques. Water 2018, 10, 46.

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